{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "import pandas as pd\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results = pd.read_json('letor_gridresults.json')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bins = np.linspace(0.2, 0.8, 50)\n",
      "results.train_score.hist(bins=bins, alpha=0.5, label='train')\n",
      "results.validation_score.hist(bins=bins, alpha=0.5, label='validation')\n",
      "plt.legend(loc='best')\n",
      "_ = plt.title(\"Distribution of NDGC@10\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BfYAWTCK/3Pn3VuB6oN55VAyb63jlHFtX186slwXo6urytI1yHYc+1MHUmpo9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\n6e//A9yLy+3rGIDVAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1115af090>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(results.train_score, results.validation_score)\n",
      "plt.xlabel('Training score')\n",
      "_ = plt.ylabel('Validation score')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8ycnJ3LNnDw8fPsyVK1eyRIlKDAqK4uuv90737Epj79699PEJocNRhVZrYTZp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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10fc9db10>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(results.training_time, results.validation_score)\n",
      "plt.xlabel('Training time')\n",
      "_ = plt.ylabel('Validation score')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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U4kSuUvG+8qN7Om2DoRfHjh3run/9+vWUJB8ajb1psTSnweBJWY6k1RrJqKhq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\nq9W/xPUb3SwhIYF6vZHuGZLt9sHZpp8pSeCFwHEMwNGbfo7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       "text": [
        "<matplotlib.figure.Figure at 0x11271c7d0>"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "Index([u'data_load_time', u'learning_rate', u'loss', u'max_depth', u'max_features', u'model_filename', u'model_save_time', u'model_size_bytes', u'prediction_time', u'subsample', u'train_score', u'training_time', u'validation_score'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "top_models = results.sort('validation_score', ascending=False)[:50]\n",
      "top_models.head(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>data_load_time</th>\n",
        "      <th>learning_rate</th>\n",
        "      <th>loss</th>\n",
        "      <th>max_depth</th>\n",
        "      <th>max_features</th>\n",
        "      <th>model_filename</th>\n",
        "      <th>model_save_time</th>\n",
        "      <th>model_size_bytes</th>\n",
        "      <th>prediction_time</th>\n",
        "      <th>subsample</th>\n",
        "      <th>train_score</th>\n",
        "      <th>training_time</th>\n",
        "      <th>validation_score</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>237</th>\n",
        "      <td> 0.046811</td>\n",
        "      <td> 0.05</td>\n",
        "      <td>    ls</td>\n",
        "      <td> 5</td>\n",
        "      <td> 100</td>\n",
        "      <td> /scratch/ogrisel/grid_jobs/8e17bbd1ff21e1fcd06...</td>\n",
        "      <td> 0.051859</td>\n",
        "      <td> 519341</td>\n",
        "      <td> 0.594188</td>\n",
        "      <td> 0.8</td>\n",
        "      <td> 0.586927</td>\n",
        "      <td> 195.013033</td>\n",
        "      <td> 0.526397</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29 </th>\n",
        "      <td> 0.066905</td>\n",
        "      <td> 0.05</td>\n",
        "      <td> huber</td>\n",
        "      <td> 5</td>\n",
        "      <td> 100</td>\n",
        "      <td> /scratch/ogrisel/grid_jobs/aa876001591bea153e4...</td>\n",
        "      <td> 0.016508</td>\n",
        "      <td> 517042</td>\n",
        "      <td> 0.486399</td>\n",
        "      <td> 0.5</td>\n",
        "      <td> 0.569554</td>\n",
        "      <td> 103.655520</td>\n",
        "      <td> 0.525084</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>66 </th>\n",
        "      <td> 0.064961</td>\n",
        "      <td> 0.05</td>\n",
        "      <td>    ls</td>\n",
        "      <td> 5</td>\n",
        "      <td>  50</td>\n",
        "      <td> /scratch/ogrisel/grid_jobs/22f2dfd319c2382811a...</td>\n",
        "      <td> 0.014300</td>\n",
        "      <td> 519384</td>\n",
        "      <td> 0.336526</td>\n",
        "      <td> 1.0</td>\n",
        "      <td> 0.584439</td>\n",
        "      <td>  76.862586</td>\n",
        "      <td> 0.524646</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>394</th>\n",
        "      <td> 0.054464</td>\n",
        "      <td> 0.05</td>\n",
        "      <td>    ls</td>\n",
        "      <td> 5</td>\n",
        "      <td>  50</td>\n",
        "      <td> /scratch/ogrisel/grid_jobs/4a66992f6fd01847259...</td>\n",
        "      <td> 0.024513</td>\n",
        "      <td> 512288</td>\n",
        "      <td> 0.375913</td>\n",
        "      <td> 0.5</td>\n",
        "      <td> 0.577110</td>\n",
        "      <td>  49.941632</td>\n",
        "      <td> 0.524414</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>294</th>\n",
        "      <td> 0.069219</td>\n",
        "      <td> 0.10</td>\n",
        "      <td>    ls</td>\n",
        "      <td> 5</td>\n",
        "      <td>  20</td>\n",
        "      <td> /scratch/ogrisel/grid_jobs/d67d59a5b3f7099c863...</td>\n",
        "      <td> 0.024924</td>\n",
        "      <td> 509243</td>\n",
        "      <td> 0.405121</td>\n",
        "      <td> 1.0</td>\n",
        "      <td> 0.604082</td>\n",
        "      <td>  44.688680</td>\n",
        "      <td> 0.523742</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>379</th>\n",
        "      <td> 0.070266</td>\n",
        "      <td> 0.05</td>\n",
        "      <td>    ls</td>\n",
        "      <td> 5</td>\n",
        "      <td>  50</td>\n",
        "      <td> /scratch/ogrisel/grid_jobs/d510a2d1b4ff500c0b1...</td>\n",
        "      <td> 0.025867</td>\n",
        "      <td> 519145</td>\n",
        "      <td> 0.428217</td>\n",
        "      <td> 0.8</td>\n",
        "      <td> 0.575713</td>\n",
        "      <td>  99.483197</td>\n",
        "      <td> 0.523702</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>138</th>\n",
        "      <td> 0.046001</td>\n",
        "      <td> 0.05</td>\n",
        "      <td>    ls</td>\n",
        "      <td> 5</td>\n",
        "      <td> 100</td>\n",
        "      <td> /scratch/ogrisel/grid_jobs/4fa6ae1a7fe80334d60...</td>\n",
        "      <td> 0.015496</td>\n",
        "      <td> 517583</td>\n",
        "      <td> 0.338197</td>\n",
        "      <td> 1.0</td>\n",
        "      <td> 0.581882</td>\n",
        "      <td> 213.198879</td>\n",
        "      <td> 0.523508</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>387</th>\n",
        "      <td> 0.047395</td>\n",
        "      <td> 0.05</td>\n",
        "      <td>    ls</td>\n",
        "      <td> 5</td>\n",
        "      <td>  20</td>\n",
        "      <td> /scratch/ogrisel/grid_jobs/14aef0de77bdf33f9f5...</td>\n",
        "      <td> 0.023361</td>\n",
        "      <td> 518827</td>\n",
        "      <td> 0.716907</td>\n",
        "      <td> 1.0</td>\n",
        "      <td> 0.580399</td>\n",
        "      <td>  48.495409</td>\n",
        "      <td> 0.523483</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>426</th>\n",
        "      <td> 0.060194</td>\n",
        "      <td> 0.05</td>\n",
        "      <td> huber</td>\n",
        "      <td> 5</td>\n",
        "      <td> 100</td>\n",
        "      <td> /scratch/ogrisel/grid_jobs/95b8e270ad6ff25f058...</td>\n",
        "      <td> 0.024757</td>\n",
        "      <td> 518748</td>\n",
        "      <td> 0.362902</td>\n",
        "      <td> 0.8</td>\n",
        "      <td> 0.574295</td>\n",
        "      <td> 179.680194</td>\n",
        "      <td> 0.523251</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>307</th>\n",
        "      <td> 0.054457</td>\n",
        "      <td> 0.05</td>\n",
        "      <td> huber</td>\n",
        "      <td> 5</td>\n",
        "      <td>  50</td>\n",
        "      <td> /scratch/ogrisel/grid_jobs/1d55f52a03a113daa14...</td>\n",
        "      <td> 0.026856</td>\n",
        "      <td> 523716</td>\n",
        "      <td> 0.479956</td>\n",
        "      <td> 0.8</td>\n",
        "      <td> 0.571901</td>\n",
        "      <td>  70.466220</td>\n",
        "      <td> 0.523189</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 13 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "     data_load_time  learning_rate   loss  max_depth  max_features  \\\n",
        "237        0.046811           0.05     ls          5           100   \n",
        "29         0.066905           0.05  huber          5           100   \n",
        "66         0.064961           0.05     ls          5            50   \n",
        "394        0.054464           0.05     ls          5            50   \n",
        "294        0.069219           0.10     ls          5            20   \n",
        "379        0.070266           0.05     ls          5            50   \n",
        "138        0.046001           0.05     ls          5           100   \n",
        "387        0.047395           0.05     ls          5            20   \n",
        "426        0.060194           0.05  huber          5           100   \n",
        "307        0.054457           0.05  huber          5            50   \n",
        "\n",
        "                                        model_filename  model_save_time  \\\n",
        "237  /scratch/ogrisel/grid_jobs/8e17bbd1ff21e1fcd06...         0.051859   \n",
        "29   /scratch/ogrisel/grid_jobs/aa876001591bea153e4...         0.016508   \n",
        "66   /scratch/ogrisel/grid_jobs/22f2dfd319c2382811a...         0.014300   \n",
        "394  /scratch/ogrisel/grid_jobs/4a66992f6fd01847259...         0.024513   \n",
        "294  /scratch/ogrisel/grid_jobs/d67d59a5b3f7099c863...         0.024924   \n",
        "379  /scratch/ogrisel/grid_jobs/d510a2d1b4ff500c0b1...         0.025867   \n",
        "138  /scratch/ogrisel/grid_jobs/4fa6ae1a7fe80334d60...         0.015496   \n",
        "387  /scratch/ogrisel/grid_jobs/14aef0de77bdf33f9f5...         0.023361   \n",
        "426  /scratch/ogrisel/grid_jobs/95b8e270ad6ff25f058...         0.024757   \n",
        "307  /scratch/ogrisel/grid_jobs/1d55f52a03a113daa14...         0.026856   \n",
        "\n",
        "     model_size_bytes  prediction_time  subsample  train_score  training_time  \\\n",
        "237            519341         0.594188        0.8     0.586927     195.013033   \n",
        "29             517042         0.486399        0.5     0.569554     103.655520   \n",
        "66             519384         0.336526        1.0     0.584439      76.862586   \n",
        "394            512288         0.375913        0.5     0.577110      49.941632   \n",
        "294            509243         0.405121        1.0     0.604082      44.688680   \n",
        "379            519145         0.428217        0.8     0.575713      99.483197   \n",
        "138            517583         0.338197        1.0     0.581882     213.198879   \n",
        "387            518827         0.716907        1.0     0.580399      48.495409   \n",
        "426            518748         0.362902        0.8     0.574295     179.680194   \n",
        "307            523716         0.479956        0.8     0.571901      70.466220   \n",
        "\n",
        "     validation_score  \n",
        "237          0.526397  \n",
        "29           0.525084  \n",
        "66           0.524646  \n",
        "394          0.524414  \n",
        "294          0.523742  \n",
        "379          0.523702  \n",
        "138          0.523508  \n",
        "387          0.523483  \n",
        "426          0.523251  \n",
        "307          0.523189  \n",
        "\n",
        "[10 rows x 13 columns]"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Impact of parameters on generalization in best models"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "parameters = [\n",
      "    'learning_rate',\n",
      "    'loss',\n",
      "    'max_depth',\n",
      "    'max_features',\n",
      "    'subsample',\n",
      "]\n",
      "\n",
      "for parameter in parameters:\n",
      "    top_models.boxplot('validation_score', parameter)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1123d0610>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1138d8f10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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JNJdIXAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1138b8cd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RlLw2+w7p3YEnYstPAn+S2KYCHASsww7yXwQeaLLfM4AfAd/AGqi5LcYrIiId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\nERERESmq/w8wgUooRlE15gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11304a110>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dsZMxVgFHTE1oMgnS2nt7T7FMKXV1p0a1g3VqFxM+jp0htgx42WQGJRPSSVt+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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x113d04650>"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Impact of parameters on training time for best models"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "parameters = [\n",
      "    'learning_rate',\n",
      "    'loss',\n",
      "    'max_depth',\n",
      "    'max_features',\n",
      "    'subsample',\n",
      "]\n",
      "\n",
      "for parameter in parameters:\n",
      "    top_models.boxplot('training_time', parameter)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1131811d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x113a6b1d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x113894710>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1138ba7d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CQ8MJR5IkSZIkSZIkSZIkSZIkSdIq+v8lLzdZZsVtWwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x113996450>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}